ArticleJournal of chemical information and modeling2016
PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.
Article in Journal of chemical information and modeling, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.Journal of cheminformatics · 2026Article
- SHREC 2025: Protein surface shape retrieval including electrostatic potential.Computers & graphics · 2025Article
- Using AlphaFold and Symmetrical Docking to Predict Protein-Protein Interactions for Exploring Potential Crystallization Conditions.Proteins · 2025Article
- Article
- Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases.Scientific reports · 2024Article
- Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints.Journal of molecular biology · 2024Article
- Recent Deep Learning Applications to Structure-Based Drug Design.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints.bioRxiv : the preprint server for biology · 2023Article
- Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.Frontiers in molecular biosciences · 2023Article
- ContactPFP: Protein function prediction using predicted contact information.Frontiers in bioinformatics · 2022Article
- Pathogenesis of IgA Nephropathy: Current Understanding and Implications for Development of Disease-Specific Treatment.Journal of clinical medicine · 2021Review
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction.Scientific reports · 2021Article
- Article
- A prospective compound screening contest identified broader inhibitors for Sirtuin 1.Scientific reports · 2019Article
- Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0.Journal of computer-aided molecular design · 2019Article
- Three-Dimensional Krawtchouk Descriptors for Protein Local Surface Shape Comparison.Pattern recognition · 2019Article
- A Novel Geometry-Based Approach to Infer Protein Interface Similarity.Scientific reports · 2018Article
- Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery.Frontiers in chemistry · 2018Review
- In silico structure-based approaches to discover protein-protein interaction-targeting drugs.Methods (San Diego, Calif.) · 2017Review
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Authors and funding
4 authors.
Funding
Abstract
Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .
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